HR Attrition

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summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

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## Type rfNews() to see new features/changes/bug fixes.

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rand_forest <- randomForest(data = employee_trainset,
                            Attrition ~ .,
                            importance = TRUE)
predict_forest <- predict(rand_forest, employee_testset)

confusionMatrix(predict_forest, 
                employee_testset$Attrition,
                positive = "Yes")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  No Yes
##        No  369  53
##        Yes   6  13
##                                           
##                Accuracy : 0.8662          
##                  95% CI : (0.8308, 0.8966)
##     No Information Rate : 0.8503          
##     P-Value [Acc > NIR] : 0.1938          
##                                           
##                   Kappa : 0.2561          
##                                           
##  Mcnemar's Test P-Value : 2.115e-09       
##                                           
##             Sensitivity : 0.19697         
##             Specificity : 0.98400         
##          Pos Pred Value : 0.68421         
##          Neg Pred Value : 0.87441         
##              Prevalence : 0.14966         
##          Detection Rate : 0.02948         
##    Detection Prevalence : 0.04308         
##       Balanced Accuracy : 0.59048         
##                                           
##        'Positive' Class : Yes             
## 

#Tables

연습 인라이 r 30

import pandas as pd
3+5

## 8

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